Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations2212
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory624.5 KiB
Average record size in memory289.1 B

Variable types

Numeric19
Categorical17

Alerts

AcceptedCmp1 is highly overall correlated with AcceptedCmpTotal and 1 other fieldsHigh correlation
AcceptedCmp2 is highly overall correlated with AcceptedCmpTotalHigh correlation
AcceptedCmp3 is highly overall correlated with AcceptedCmpTotal and 1 other fieldsHigh correlation
AcceptedCmp4 is highly overall correlated with AcceptedCmpTotal and 1 other fieldsHigh correlation
AcceptedCmp5 is highly overall correlated with AcceptedCmpTotal and 5 other fieldsHigh correlation
AcceptedCmpTotal is highly overall correlated with AcceptedCmp1 and 5 other fieldsHigh correlation
Age is highly overall correlated with AgeGroupHigh correlation
AgeGroup is highly overall correlated with AgeHigh correlation
Children is highly overall correlated with HasChildren and 2 other fieldsHigh correlation
DaysSinceEnrolled is highly overall correlated with YearsSinceEnrolledHigh correlation
HasAcceptedCmp is highly overall correlated with AcceptedCmp1 and 4 other fieldsHigh correlation
HasChildren is highly overall correlated with Children and 8 other fieldsHigh correlation
Income is highly overall correlated with AcceptedCmp5 and 14 other fieldsHigh correlation
Kidhome is highly overall correlated with Children and 1 other fieldsHigh correlation
MntFishProducts is highly overall correlated with Income and 10 other fieldsHigh correlation
MntFruits is highly overall correlated with Income and 10 other fieldsHigh correlation
MntGoldProds is highly overall correlated with Income and 11 other fieldsHigh correlation
MntMeatProducts is highly overall correlated with HasChildren and 12 other fieldsHigh correlation
MntRegularProds is highly overall correlated with AcceptedCmp5 and 13 other fieldsHigh correlation
MntSweetProducts is highly overall correlated with Income and 10 other fieldsHigh correlation
MntTotal is highly overall correlated with AcceptedCmp5 and 13 other fieldsHigh correlation
MntWines is highly overall correlated with AcceptedCmp5 and 12 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 12 other fieldsHigh correlation
NumDealsPurchases is highly overall correlated with HasChildrenHigh correlation
NumStorePurchases is highly overall correlated with Income and 11 other fieldsHigh correlation
NumTotalPurchase is highly overall correlated with Income and 11 other fieldsHigh correlation
NumWebPurchases is highly overall correlated with Income and 8 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with HasChildren and 2 other fieldsHigh correlation
Teenhome is highly overall correlated with Children and 1 other fieldsHigh correlation
YearsSinceEnrolled is highly overall correlated with DaysSinceEnrolledHigh correlation
AcceptedCmp3 is highly imbalanced (62.0%)Imbalance
AcceptedCmp4 is highly imbalanced (61.9%)Imbalance
AcceptedCmp5 is highly imbalanced (62.4%)Imbalance
AcceptedCmp1 is highly imbalanced (65.6%)Imbalance
AcceptedCmp2 is highly imbalanced (89.6%)Imbalance
Complain is highly imbalanced (92.6%)Imbalance
AcceptedCmpTotal is highly imbalanced (57.1%)Imbalance
ID has unique valuesUnique
Recency has 28 (1.3%) zerosZeros
MntFruits has 394 (17.8%) zerosZeros
MntFishProducts has 379 (17.1%) zerosZeros
MntSweetProducts has 412 (18.6%) zerosZeros
MntGoldProds has 61 (2.8%) zerosZeros
NumDealsPurchases has 44 (2.0%) zerosZeros
NumWebPurchases has 48 (2.2%) zerosZeros
NumCatalogPurchases has 575 (26.0%) zerosZeros

Reproduction

Analysis started2024-10-01 01:18:38.526281
Analysis finished2024-10-01 01:19:52.465861
Duration1 minute and 13.94 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct2212
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5585.1609
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:52.707432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile571.75
Q12814.75
median5454.5
Q38418.5
95-th percentile10675.45
Maximum11191
Range11191
Interquartile range (IQR)5603.75

Descriptive statistics

Standard deviation3247.5237
Coefficient of variation (CV)0.58145571
Kurtosis-1.1881653
Mean5585.1609
Median Absolute Deviation (MAD)2783
Skewness0.041710598
Sum12354376
Variance10546410
MonotonicityNot monotonic
2024-09-30T22:19:52.934510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9405 1
 
< 0.1%
5524 1
 
< 0.1%
2174 1
 
< 0.1%
4141 1
 
< 0.1%
7366 1
 
< 0.1%
6261 1
 
< 0.1%
9246 1
 
< 0.1%
4838 1
 
< 0.1%
9589 1
 
< 0.1%
736 1
 
< 0.1%
Other values (2202) 2202
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Graduation
1115 
PhD
480 
Master
365 
2n Cycle
198 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5198915
Min length3

Characters and Unicode

Total characters16634
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation 1115
50.4%
PhD 480
21.7%
Master 365
 
16.5%
2n Cycle 198
 
9.0%
Basic 54
 
2.4%

Length

2024-09-30T22:19:53.158565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:19:53.349208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1115
46.3%
phd 480
19.9%
master 365
 
15.1%
2n 198
 
8.2%
cycle 198
 
8.2%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1313
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
u 1115
 
6.7%
d 1115
 
6.7%
o 1115
 
6.7%
e 563
 
3.4%
Other values (12) 3520
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1313
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
u 1115
 
6.7%
d 1115
 
6.7%
o 1115
 
6.7%
e 563
 
3.4%
Other values (12) 3520
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1313
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
u 1115
 
6.7%
d 1115
 
6.7%
o 1115
 
6.7%
e 563
 
3.4%
Other values (12) 3520
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1313
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
u 1115
 
6.7%
d 1115
 
6.7%
o 1115
 
6.7%
e 563
 
3.4%
Other values (12) 3520
21.2%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
1
1428 
0
784 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1428
64.6%
0 784
35.4%

Length

2024-09-30T22:19:53.549732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:19:53.702338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1428
64.6%
0 784
35.4%

Most occurring characters

ValueCountFrequency (%)
1 1428
64.6%
0 784
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1428
64.6%
0 784
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1428
64.6%
0 784
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1428
64.6%
0 784
35.4%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct1970
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51958.811
Minimum1730
Maximum162397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:53.901742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18983.5
Q135233.5
median51371
Q368487
95-th percentile84007
Maximum162397
Range160667
Interquartile range (IQR)33253.5

Descriptive statistics

Standard deviation21527.279
Coefficient of variation (CV)0.41431431
Kurtosis0.71643065
Mean51958.811
Median Absolute Deviation (MAD)16547
Skewness0.34802798
Sum1.1493289 × 108
Variance4.6342373 × 108
MonotonicityNot monotonic
2024-09-30T22:19:54.142884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
47025 3
 
0.1%
67445 3
 
0.1%
83844 3
 
0.1%
39922 3
 
0.1%
80134 3
 
0.1%
48432 3
 
0.1%
37760 3
 
0.1%
46098 3
 
0.1%
Other values (1960) 2172
98.2%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%
102692 1
< 0.1%

Kidhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1281 
1
885 
2
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1281
57.9%
1 885
40.0%
2 46
 
2.1%

Length

2024-09-30T22:19:54.387049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:19:54.545977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1281
57.9%
1 885
40.0%
2 46
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1281
57.9%
1 885
40.0%
2 46
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1281
57.9%
1 885
40.0%
2 46
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1281
57.9%
1 885
40.0%
2 46
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1281
57.9%
1 885
40.0%
2 46
 
2.1%

Teenhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1144 
1
1017 
2
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1144
51.7%
1 1017
46.0%
2 51
 
2.3%

Length

2024-09-30T22:19:54.732995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:19:54.892672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1144
51.7%
1 1017
46.0%
2 51
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1144
51.7%
1 1017
46.0%
2 51
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1144
51.7%
1 1017
46.0%
2 51
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1144
51.7%
1 1017
46.0%
2 51
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1144
51.7%
1 1017
46.0%
2 51
 
2.3%

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.019439
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:55.094497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.943121
Coefficient of variation (CV)0.59044171
Kurtosis-1.1998493
Mean49.019439
Median Absolute Deviation (MAD)25
Skewness-0.00068798996
Sum108431
Variance837.70428
MonotonicityNot monotonic
2024-09-30T22:19:55.884612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.4%
65 30
 
1.4%
29 29
 
1.3%
71 29
 
1.3%
49 29
 
1.3%
3 29
 
1.3%
Other values (90) 1904
86.1%
ValueCountFrequency (%)
0 28
1.3%
1 24
1.1%
2 28
1.3%
3 29
1.3%
4 26
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 16
0.7%
98 22
1.0%
97 20
0.9%
96 23
1.0%
95 18
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.4%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

HIGH CORRELATION 

Distinct775
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.28752
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:56.121983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median175.5
Q3505
95-th percentile1000.45
Maximum1493
Range1493
Interquartile range (IQR)481

Descriptive statistics

Standard deviation337.32294
Coefficient of variation (CV)1.1049352
Kurtosis0.58449992
Mean305.28752
Median Absolute Deviation (MAD)166.5
Skewness1.1709772
Sum675296
Variance113786.77
MonotonicityNot monotonic
2024-09-30T22:19:56.351851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
6 37
 
1.7%
5 37
 
1.7%
1 37
 
1.7%
4 33
 
1.5%
3 30
 
1.4%
8 29
 
1.3%
9 27
 
1.2%
12 25
 
1.1%
10 24
 
1.1%
Other values (765) 1891
85.5%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.4%
4 33
1.5%
5 37
1.7%
6 37
1.7%
7 21
0.9%
8 29
1.3%
9 27
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.329566
Minimum0
Maximum199
Zeros394
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:56.722836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.744052
Coefficient of variation (CV)1.5094837
Kurtosis4.0731063
Mean26.329566
Median Absolute Deviation (MAD)8
Skewness2.1038643
Sum58241
Variance1579.5897
MonotonicityNot monotonic
2024-09-30T22:19:56.958714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 394
 
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.2%
4 103
 
4.7%
7 67
 
3.0%
5 62
 
2.8%
6 61
 
2.8%
12 50
 
2.3%
8 48
 
2.2%
Other values (148) 1036
46.8%
ValueCountFrequency (%)
0 394
17.8%
1 158
7.1%
2 119
 
5.4%
3 114
 
5.2%
4 103
 
4.7%
5 62
 
2.8%
6 61
 
2.8%
7 67
 
3.0%
8 48
 
2.2%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

HIGH CORRELATION 

Distinct553
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.02984
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:57.189227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median68
Q3232.25
95-th percentile687.9
Maximum1725
Range1725
Interquartile range (IQR)216.25

Descriptive statistics

Standard deviation224.25449
Coefficient of variation (CV)1.3426014
Kurtosis5.068315
Mean167.02984
Median Absolute Deviation (MAD)60
Skewness2.027563
Sum369470
Variance50290.078
MonotonicityNot monotonic
2024-09-30T22:19:57.417098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 49
 
2.2%
11 49
 
2.2%
8 44
 
2.0%
6 42
 
1.9%
10 40
 
1.8%
3 39
 
1.8%
9 37
 
1.7%
16 35
 
1.6%
12 34
 
1.5%
Other values (543) 1790
80.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.4%
3 39
1.8%
4 30
1.4%
5 49
2.2%
6 42
1.9%
7 53
2.4%
8 44
2.0%
9 37
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%
946 1
< 0.1%

MntFishProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.648734
Minimum0
Maximum259
Zeros379
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:57.649023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.772033
Coefficient of variation (CV)1.4548174
Kurtosis3.0759672
Mean37.648734
Median Absolute Deviation (MAD)12
Skewness1.9165585
Sum83279
Variance2999.9756
MonotonicityNot monotonic
2024-09-30T22:19:57.874507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
17.1%
2 152
 
6.9%
3 128
 
5.8%
4 108
 
4.9%
6 81
 
3.7%
7 64
 
2.9%
8 57
 
2.6%
10 54
 
2.4%
13 48
 
2.2%
11 46
 
2.1%
Other values (172) 1095
49.5%
ValueCountFrequency (%)
0 379
17.1%
1 10
 
0.5%
2 152
6.9%
3 128
 
5.8%
4 108
 
4.9%
5 1
 
< 0.1%
6 81
 
3.7%
7 64
 
2.9%
8 57
 
2.6%
10 54
 
2.4%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.046564
Minimum0
Maximum262
Zeros412
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:58.090783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile125.45
Maximum262
Range262
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.090991
Coefficient of variation (CV)1.5192684
Kurtosis4.1017763
Mean27.046564
Median Absolute Deviation (MAD)8
Skewness2.1029247
Sum59827
Variance1688.4696
MonotonicityNot monotonic
2024-09-30T22:19:58.314816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 412
 
18.6%
1 160
 
7.2%
2 123
 
5.6%
3 101
 
4.6%
4 79
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
12 45
 
2.0%
Other values (166) 1051
47.5%
ValueCountFrequency (%)
0 412
18.6%
1 160
 
7.2%
2 123
 
5.6%
3 101
 
4.6%
4 79
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%
188 1
 
< 0.1%

MntGoldProds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct212
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.925859
Minimum0
Maximum321
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:58.528214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24.5
Q356
95-th percentile163.9
Maximum321
Range321
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.706981
Coefficient of variation (CV)1.1771422
Kurtosis3.158125
Mean43.925859
Median Absolute Deviation (MAD)18.5
Skewness1.8377282
Sum97164
Variance2673.6119
MonotonicityNot monotonic
2024-09-30T22:19:58.745640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 71
 
3.2%
4 69
 
3.1%
3 68
 
3.1%
5 63
 
2.8%
12 62
 
2.8%
0 61
 
2.8%
2 61
 
2.8%
6 55
 
2.5%
7 52
 
2.4%
10 49
 
2.2%
Other values (202) 1601
72.4%
ValueCountFrequency (%)
0 61
2.8%
1 71
3.2%
2 61
2.8%
3 68
3.1%
4 69
3.1%
5 63
2.8%
6 55
2.5%
7 52
2.4%
8 40
1.8%
9 43
1.9%
ValueCountFrequency (%)
321 1
 
< 0.1%
291 1
 
< 0.1%
262 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
245 1
 
< 0.1%
242 2
 
0.1%
241 6
0.3%

NumDealsPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3245931
Minimum0
Maximum15
Zeros44
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:58.934068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9245072
Coefficient of variation (CV)0.82788989
Kurtosis8.9706996
Mean2.3245931
Median Absolute Deviation (MAD)1
Skewness2.4151853
Sum5142
Variance3.7037278
MonotonicityNot monotonic
2024-09-30T22:19:59.144689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 957
43.3%
2 493
22.3%
3 293
 
13.2%
4 187
 
8.5%
5 94
 
4.2%
6 60
 
2.7%
0 44
 
2.0%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 23
 
1.0%
ValueCountFrequency (%)
0 44
 
2.0%
1 957
43.3%
2 493
22.3%
3 293
 
13.2%
4 187
 
8.5%
5 94
 
4.2%
6 60
 
2.7%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 3
 
0.1%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 39
1.8%
6 60
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0881555
Minimum0
Maximum27
Zeros48
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:59.330477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7421868
Coefficient of variation (CV)0.67076381
Kurtosis4.0662932
Mean4.0881555
Median Absolute Deviation (MAD)2
Skewness1.195317
Sum9043
Variance7.5195883
MonotonicityNot monotonic
2024-09-30T22:19:59.525360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 367
16.6%
1 347
15.7%
3 333
15.1%
4 276
12.5%
5 219
9.9%
6 201
9.1%
7 154
7.0%
8 102
 
4.6%
9 75
 
3.4%
0 48
 
2.2%
Other values (5) 90
 
4.1%
ValueCountFrequency (%)
0 48
 
2.2%
1 347
15.7%
2 367
16.6%
3 333
15.1%
4 276
12.5%
5 219
9.9%
6 201
9.1%
7 154
7.0%
8 102
 
4.6%
9 75
 
3.4%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.4%
8 102
4.6%
7 154
7.0%
6 201
9.1%
5 219
9.9%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6722423
Minimum0
Maximum28
Zeros575
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:19:59.694856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9275416
Coefficient of variation (CV)1.0955375
Kurtosis8.0706677
Mean2.6722423
Median Absolute Deviation (MAD)2
Skewness1.8815233
Sum5911
Variance8.5705001
MonotonicityNot monotonic
2024-09-30T22:19:59.849254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 575
26.0%
1 490
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 127
 
5.7%
7 79
 
3.6%
8 55
 
2.5%
10 47
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 575
26.0%
1 490
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 127
 
5.7%
7 79
 
3.6%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.9%
10 47
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.6%
6 127
5.7%
5 137
6.2%
4 181
8.2%

NumStorePurchases
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8065099
Minimum0
Maximum13
Zeros14
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:00.028499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509391
Coefficient of variation (CV)0.55987834
Kurtosis-0.62937232
Mean5.8065099
Median Absolute Deviation (MAD)2
Skewness0.69971471
Sum12844
Variance10.568605
MonotonicityNot monotonic
2024-09-30T22:20:00.215736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 483
21.8%
4 318
14.4%
2 218
9.9%
5 211
9.5%
6 177
 
8.0%
8 147
 
6.6%
7 141
 
6.4%
10 124
 
5.6%
9 106
 
4.8%
12 104
 
4.7%
Other values (4) 183
 
8.3%
ValueCountFrequency (%)
0 14
 
0.6%
1 6
 
0.3%
2 218
9.9%
3 483
21.8%
4 318
14.4%
5 211
9.5%
6 177
 
8.0%
7 141
 
6.4%
8 147
 
6.6%
9 106
 
4.8%
ValueCountFrequency (%)
13 83
 
3.8%
12 104
 
4.7%
11 80
 
3.6%
10 124
 
5.6%
9 106
 
4.8%
8 147
6.6%
7 141
6.4%
6 177
8.0%
5 211
9.5%
4 318
14.4%

NumWebVisitsMonth
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3214286
Minimum0
Maximum20
Zeros10
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:00.392064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.425597
Coefficient of variation (CV)0.45581688
Kurtosis1.8540001
Mean5.3214286
Median Absolute Deviation (MAD)2
Skewness0.21804021
Sum11771
Variance5.8835207
MonotonicityNot monotonic
2024-09-30T22:20:00.585403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 387
17.5%
8 340
15.4%
6 334
15.1%
5 278
12.6%
4 216
9.8%
3 203
9.2%
2 201
9.1%
1 149
 
6.7%
9 82
 
3.7%
0 10
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 10
 
0.5%
1 149
 
6.7%
2 201
9.1%
3 203
9.2%
4 216
9.8%
5 278
12.6%
6 334
15.1%
7 387
17.5%
8 340
15.4%
9 82
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 82
 
3.7%
8 340
15.4%
7 387
17.5%
6 334
15.1%

AcceptedCmp3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2049 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2049
92.6%
1 163
 
7.4%

Length

2024-09-30T22:20:00.786412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:00.941094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2049
92.6%
1 163
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2049
92.6%
1 163
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2049
92.6%
1 163
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2049
92.6%
1 163
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2049
92.6%
1 163
 
7.4%

AcceptedCmp4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2048 
1
 
164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2048
92.6%
1 164
 
7.4%

Length

2024-09-30T22:20:01.112371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:01.265943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2048
92.6%
1 164
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2048
92.6%
1 164
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2048
92.6%
1 164
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2048
92.6%
1 164
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2048
92.6%
1 164
 
7.4%

AcceptedCmp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2051 
1
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2051
92.7%
1 161
 
7.3%

Length

2024-09-30T22:20:01.461788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:01.613794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2051
92.7%
1 161
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2051
92.7%
1 161
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2051
92.7%
1 161
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2051
92.7%
1 161
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2051
92.7%
1 161
 
7.3%

AcceptedCmp1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2070 
1
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2070
93.6%
1 142
 
6.4%

Length

2024-09-30T22:20:01.780012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:01.935382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2070
93.6%
1 142
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2070
93.6%
1 142
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2070
93.6%
1 142
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2070
93.6%
1 142
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2070
93.6%
1 142
 
6.4%

AcceptedCmp2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2182 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2182
98.6%
1 30
 
1.4%

Length

2024-09-30T22:20:02.115634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:02.374823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2182
98.6%
1 30
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2182
98.6%
1 30
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2182
98.6%
1 30
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2182
98.6%
1 30
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2182
98.6%
1 30
 
1.4%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2192 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2192
99.1%
1 20
 
0.9%

Length

2024-09-30T22:20:02.708236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:02.865585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2192
99.1%
1 20
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2192
99.1%
1 20
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2192
99.1%
1 20
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2192
99.1%
1 20
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2192
99.1%
1 20
 
0.9%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1879 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1879
84.9%
1 333
 
15.1%

Length

2024-09-30T22:20:03.053338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:03.231400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1879
84.9%
1 333
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1879
84.9%
1 333
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1879
84.9%
1 333
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1879
84.9%
1 333
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1879
84.9%
1 333
 
15.1%

DaysSinceEnrolled
Real number (ℝ)

HIGH CORRELATION 

Distinct662
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.71429
Minimum0
Maximum699
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:03.428802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1180
median356
Q3529
95-th percentile667
Maximum699
Range699
Interquartile range (IQR)349

Descriptive statistics

Standard deviation202.49489
Coefficient of variation (CV)0.5724815
Kurtosis-1.2011499
Mean353.71429
Median Absolute Deviation (MAD)174.5
Skewness-0.018120606
Sum782416
Variance41004.179
MonotonicityNot monotonic
2024-09-30T22:20:03.667257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667 12
 
0.5%
500 11
 
0.5%
655 11
 
0.5%
48 11
 
0.5%
313 10
 
0.5%
38 10
 
0.5%
543 9
 
0.4%
120 9
 
0.4%
98 9
 
0.4%
85 9
 
0.4%
Other values (652) 2111
95.4%
ValueCountFrequency (%)
0 2
 
0.1%
1 3
0.1%
2 3
0.1%
3 4
0.2%
4 5
0.2%
5 2
 
0.1%
6 2
 
0.1%
7 5
0.2%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
699 1
 
< 0.1%
698 1
 
< 0.1%
697 4
0.2%
696 3
0.1%
695 5
0.2%
694 4
0.2%
693 1
 
< 0.1%
692 3
0.1%
691 4
0.2%
690 7
0.3%

YearsSinceEnrolled
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1141 
1
1071 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1141
51.6%
1 1071
48.4%

Length

2024-09-30T22:20:03.916584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:04.079355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1141
51.6%
1 1071
48.4%

Most occurring characters

ValueCountFrequency (%)
0 1141
51.6%
1 1071
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1141
51.6%
1 1071
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1141
51.6%
1 1071
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1141
51.6%
1 1071
48.4%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.086347
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:04.279885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q137
median44
Q355
95-th percentile64
Maximum74
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.701599
Coefficient of variation (CV)0.25953752
Kurtosis-0.79585816
Mean45.086347
Median Absolute Deviation (MAD)9
Skewness0.093406858
Sum99731
Variance136.92741
MonotonicityNot monotonic
2024-09-30T22:20:04.500403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 89
 
4.0%
43 86
 
3.9%
39 83
 
3.8%
42 78
 
3.5%
36 76
 
3.4%
44 75
 
3.4%
49 74
 
3.3%
41 72
 
3.3%
45 70
 
3.2%
40 69
 
3.1%
Other values (46) 1440
65.1%
ValueCountFrequency (%)
18 2
 
0.1%
19 5
 
0.2%
20 3
 
0.1%
21 5
 
0.2%
22 13
0.6%
23 15
0.7%
24 18
0.8%
25 29
1.3%
26 29
1.3%
27 27
1.2%
ValueCountFrequency (%)
74 1
 
< 0.1%
73 1
 
< 0.1%
71 6
 
0.3%
70 7
 
0.3%
69 8
 
0.4%
68 16
0.7%
67 16
0.7%
66 21
0.9%
65 30
1.4%
64 29
1.3%

AgeGroup
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
31-45
964 
46-60
727 
61+
264 
18-30
257 

Length

Max length5
Median length5
Mean length4.761302
Min length3

Characters and Unicode

Total characters10532
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46-60
2nd row46-60
3rd row46-60
4th row18-30
5th row31-45

Common Values

ValueCountFrequency (%)
31-45 964
43.6%
46-60 727
32.9%
61+ 264
 
11.9%
18-30 257
 
11.6%

Length

2024-09-30T22:20:04.757700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:04.954206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31-45 964
43.6%
46-60 727
32.9%
61 264
 
11.9%
18-30 257
 
11.6%

Most occurring characters

ValueCountFrequency (%)
- 1948
18.5%
6 1718
16.3%
4 1691
16.1%
1 1485
14.1%
3 1221
11.6%
0 984
9.3%
5 964
9.2%
+ 264
 
2.5%
8 257
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1948
18.5%
6 1718
16.3%
4 1691
16.1%
1 1485
14.1%
3 1221
11.6%
0 984
9.3%
5 964
9.2%
+ 264
 
2.5%
8 257
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1948
18.5%
6 1718
16.3%
4 1691
16.1%
1 1485
14.1%
3 1221
11.6%
0 984
9.3%
5 964
9.2%
+ 264
 
2.5%
8 257
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1948
18.5%
6 1718
16.3%
4 1691
16.1%
1 1485
14.1%
3 1221
11.6%
0 984
9.3%
5 964
9.2%
+ 264
 
2.5%
8 257
 
2.4%

MntTotal
Real number (ℝ)

HIGH CORRELATION 

Distinct1047
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607.26808
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:05.172507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median397
Q31048
95-th percentile1774.7
Maximum2525
Range2520
Interquartile range (IQR)979

Descriptive statistics

Standard deviation602.51336
Coefficient of variation (CV)0.99217031
Kurtosis-0.34526879
Mean607.26808
Median Absolute Deviation (MAD)354
Skewness0.85738245
Sum1343277
Variance363022.35
MonotonicityNot monotonic
2024-09-30T22:20:05.469409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 18
 
0.8%
22 17
 
0.8%
57 16
 
0.7%
44 15
 
0.7%
55 15
 
0.7%
20 14
 
0.6%
37 14
 
0.6%
48 14
 
0.6%
38 14
 
0.6%
43 14
 
0.6%
Other values (1037) 2061
93.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
 
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.5%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%

MntRegularProds
Real number (ℝ)

HIGH CORRELATION 

Distinct898
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563.34222
Minimum4
Maximum2491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:05.707821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q155.75
median343
Q3964
95-th percentile1700.35
Maximum2491
Range2487
Interquartile range (IQR)908.25

Descriptive statistics

Standard deviation576.9343
Coefficient of variation (CV)1.0241276
Kurtosis-0.22932859
Mean563.34222
Median Absolute Deviation (MAD)310
Skewness0.91383134
Sum1246113
Variance332853.18
MonotonicityNot monotonic
2024-09-30T22:20:05.954016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 30
 
1.4%
41 25
 
1.1%
19 24
 
1.1%
16 24
 
1.1%
40 24
 
1.1%
44 20
 
0.9%
20 19
 
0.9%
32 17
 
0.8%
37 16
 
0.7%
17 16
 
0.7%
Other values (888) 1997
90.3%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 2
 
0.1%
8 7
0.3%
9 6
0.3%
10 5
0.2%
11 7
0.3%
12 8
0.4%
13 6
0.3%
ValueCountFrequency (%)
2491 1
< 0.1%
2429 2
0.1%
2304 2
0.1%
2262 1
< 0.1%
2244 1
< 0.1%
2188 1
< 0.1%
2169 1
< 0.1%
2158 1
< 0.1%
2157 1
< 0.1%
2153 1
< 0.1%

Children
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
1
1114 
0
632 
2
416 
3
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1114
50.4%
0 632
28.6%
2 416
 
18.8%
3 50
 
2.3%

Length

2024-09-30T22:20:06.171423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:06.367416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1114
50.4%
0 632
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 1114
50.4%
0 632
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1114
50.4%
0 632
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1114
50.4%
0 632
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1114
50.4%
0 632
28.6%
2 416
 
18.8%
3 50
 
2.3%

HasChildren
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
1
1580 
0
632 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1580
71.4%
0 632
 
28.6%

Length

2024-09-30T22:20:06.578366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:06.729801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1580
71.4%
0 632
 
28.6%

Most occurring characters

ValueCountFrequency (%)
1 1580
71.4%
0 632
 
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1580
71.4%
0 632
 
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1580
71.4%
0 632
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1580
71.4%
0 632
 
28.6%

AcceptedCmpTotal
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1754 
1
322 
2
 
81
3
 
44
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1754
79.3%
1 322
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Length

2024-09-30T22:20:06.920220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:07.087941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1754
79.3%
1 322
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1754
79.3%
1 322
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1754
79.3%
1 322
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1754
79.3%
1 322
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1754
79.3%
1 322
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

HasAcceptedCmp
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1754 
1
458 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2212
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1754
79.3%
1 458
 
20.7%

Length

2024-09-30T22:20:07.282437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-30T22:20:07.445526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1754
79.3%
1 458
 
20.7%

Most occurring characters

ValueCountFrequency (%)
0 1754
79.3%
1 458
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1754
79.3%
1 458
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1754
79.3%
1 458
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1754
79.3%
1 458
 
20.7%

NumTotalPurchase
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.566908
Minimum0
Maximum32
Zeros6
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2024-09-30T22:20:07.628956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median12
Q318.25
95-th percentile24
Maximum32
Range32
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation7.2054272
Coefficient of variation (CV)0.57336517
Kurtosis-1.1220579
Mean12.566908
Median Absolute Deviation (MAD)6
Skewness0.29235667
Sum27798
Variance51.918181
MonotonicityNot monotonic
2024-09-30T22:20:07.836256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4 200
 
9.0%
6 188
 
8.5%
5 178
 
8.0%
7 126
 
5.7%
3 124
 
5.6%
18 102
 
4.6%
16 98
 
4.4%
14 97
 
4.4%
17 88
 
4.0%
21 85
 
3.8%
Other values (23) 926
41.9%
ValueCountFrequency (%)
0 6
 
0.3%
1 5
 
0.2%
2 2
 
0.1%
3 124
5.6%
4 200
9.0%
5 178
8.0%
6 188
8.5%
7 126
5.7%
8 50
 
2.3%
9 44
 
2.0%
ValueCountFrequency (%)
32 3
 
0.1%
31 2
 
0.1%
30 2
 
0.1%
29 6
 
0.3%
28 10
 
0.5%
27 22
 
1.0%
26 24
 
1.1%
25 39
1.8%
24 52
2.4%
23 63
2.8%

Interactions

2024-09-30T22:19:48.025100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:43.921957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:47.258666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:51.970449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:56.761553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:01.103479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:04.475286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:07.781865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:11.402993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:14.567841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:17.807287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:21.491393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:24.699064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:27.964511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:31.222894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:34.596922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:38.362185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:41.594682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:44.829037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:48.185098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:44.084654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:47.450799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:52.211148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:56.958847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:01.272185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:04.639305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:07.935517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:11.562395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:14.715935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:17.969360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:21.650615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:24.857541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:28.151908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:31.399127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:34.763644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:38.518585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:41.763085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:44.994844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:48.374451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:44.250481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:48.791974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:52.472393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:57.147200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:01.459503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:04.828264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:08.115711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:11.737988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:14.896667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:18.152895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:21.836639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:25.041817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:28.336353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:31.586483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:34.944429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:38.689798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:41.940523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:45.174430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:48.534389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:44.415998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:48.980940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:52.699063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:57.420469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:01.621846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:04.999778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:08.273841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:11.898412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:15.057023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:18.319553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:21.989341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:25.290543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:28.494011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:31.750947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:35.109694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:38.850730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:42.108666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:45.332314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:48.704769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:44.594215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:49.208747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:52.957400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:57.716320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:01.836173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:05.185622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:08.443055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:12.063968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:15.228084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:18.521190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:22.160389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:25.459942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:28.667510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:31.930552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:35.307976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:39.013230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:42.273861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:45.503756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:48.882824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:44.769568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:49.425611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:53.211184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:57.999550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:02.025203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:05.367656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:08.613301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:12.242962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:15.398613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:18.701200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:22.329772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:25.656342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:28.841119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:32.116003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:35.489842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:39.189410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:42.485101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:45.678642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:49.049346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:44.963591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:49.625848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:53.413689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:58.325650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:02.201084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:05.553898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:09.098379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:12.410477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:15.573383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:18.879475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:22.503581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:25.855889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:29.015067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:32.289521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:35.670683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:39.363008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:42.649626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:45.849649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:49.208568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:45.144029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:49.809493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:53.578613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:58.534753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:02.372986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:05.715151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:09.258914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:12.565303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:15.731323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:19.049686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:22.657250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:26.009232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:29.185965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:32.459673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:35.835426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:39.513968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:42.818431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:46.012609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:49.369651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:45.323844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:49.981841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:53.803131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:18:58.708523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:02.529585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:05.879718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-30T22:19:41.434947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:44.632458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-30T22:19:47.858190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-30T22:20:08.124647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AcceptedCmp1AcceptedCmp2AcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmpTotalAgeAgeGroupChildrenComplainDaysSinceEnrolledEducationHasAcceptedCmpHasChildrenIDIncomeKidhomeMarital_StatusMntFishProductsMntFruitsMntGoldProdsMntMeatProductsMntRegularProdsMntSweetProductsMntTotalMntWinesNumCatalogPurchasesNumDealsPurchasesNumStorePurchasesNumTotalPurchaseNumWebPurchasesNumWebVisitsMonthRecencyResponseTeenhomeYearsSinceEnrolled
AcceptedCmp11.0000.1670.0900.2380.4050.6920.0590.0500.2780.0000.0000.0320.5100.2770.0450.4010.1850.0000.2700.2600.2000.3110.4070.2560.4180.3560.3150.1670.1950.2700.1660.2040.0000.2940.1470.000
AcceptedCmp20.1671.0000.0610.2870.2140.6690.0350.0190.0740.0000.0000.0170.2240.0740.0360.1450.0790.0000.0480.0000.0850.0330.1600.0470.1520.3010.1120.0000.0810.1030.0000.0000.0340.1620.0000.000
AcceptedCmp30.0900.0611.0000.0740.0750.5670.0570.0620.0000.0000.0080.0000.5500.0000.0000.0650.0300.0000.0760.0030.1260.0270.0640.0000.0560.0940.0880.0000.1810.0660.0210.0770.0440.2510.0380.000
AcceptedCmp40.2380.2870.0741.0000.3090.6170.0630.0600.0830.0000.0080.0530.5510.0720.0000.2670.1620.0000.0120.0700.0680.0960.2640.0250.2480.3970.1900.0590.2130.2220.1590.0000.0000.1760.0240.000
AcceptedCmp50.4050.2140.0750.3091.0000.7380.0970.0630.3450.0000.0000.0320.5460.3440.0000.5620.2090.0000.2630.2800.1780.3760.5200.2660.5240.5180.3580.2440.2290.2880.1700.3060.0000.3220.2040.000
AcceptedCmpTotal0.6920.6690.5670.6170.7381.0000.0560.0480.1590.0000.0150.0000.9990.2780.0000.2470.1510.0000.1190.1310.1100.1590.2610.1170.2660.3030.1900.0770.1370.1620.1100.0930.0000.4260.0940.000
Age0.0590.0350.0570.0630.0970.0561.0000.9030.2140.028-0.0140.1130.0740.316-0.0050.2180.2330.0900.0290.0260.0760.1140.167-0.0020.1600.2360.1790.0880.1700.1810.168-0.1320.0170.0650.3360.043
AgeGroup0.0500.0190.0620.0600.0630.0480.9031.0000.1640.0000.0000.1070.0240.2120.0000.1670.2060.0690.0600.0310.0270.0850.1160.0410.1090.1100.0820.0790.1090.1090.0920.0860.0640.0270.3100.000
Children0.2780.0740.0000.0830.3450.1590.2140.1641.0000.0000.0320.0340.2391.0000.0000.3260.6210.0480.2860.2670.1610.3480.3230.2480.3240.2170.2920.3680.2010.2340.1490.3240.0310.2030.5640.000
Complain0.0000.0000.0000.0000.0000.0000.0280.0000.0001.0000.0540.0300.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.000
DaysSinceEnrolled0.0000.0000.0080.0080.0000.015-0.0140.0000.0320.0541.0000.0460.0220.000-0.002-0.0230.0400.0000.1320.1280.2250.1550.1730.1150.1830.1550.1260.2160.1140.1580.2000.3050.0260.2010.0000.961
Education0.0320.0170.0000.0530.0320.0000.1130.1070.0340.0300.0461.0000.0300.0000.0000.1820.0500.0000.0650.0730.0660.0550.0860.0690.0920.1140.0650.0000.1070.1010.0820.0540.0000.0930.1060.058
HasAcceptedCmp0.5100.2240.5500.5510.5460.9990.0740.0240.2390.0000.0220.0301.0000.2350.0210.3770.2040.0000.1790.1800.2070.2740.4170.1750.4200.4730.3330.1360.2450.2990.2160.1380.0000.3660.1040.000
HasChildren0.2770.0740.0000.0720.3440.2780.3160.2121.0000.0000.0000.0000.2351.0000.0000.5370.5380.0530.4800.4530.2530.5950.5330.4190.5320.3480.4820.5640.3160.3530.2110.5570.0210.2010.6100.000
ID0.0450.0360.0000.0000.0000.000-0.0050.0000.0000.000-0.0020.0000.0210.0001.0000.0040.0000.000-0.029-0.018-0.039-0.011-0.023-0.030-0.022-0.022-0.009-0.027-0.020-0.014-0.023-0.012-0.0440.0340.0000.000
Income0.4010.1450.0650.2670.5620.2470.2180.1670.3260.000-0.0230.1820.3770.5370.0041.0000.4030.0360.5780.5830.5060.8190.8620.5690.8530.8320.793-0.1960.7350.7800.574-0.6440.0100.2580.3290.000
Kidhome0.1850.0790.0300.1620.2090.1510.2330.2060.6210.0220.0400.0500.2040.5380.0000.4031.0000.0000.3240.3130.2690.3220.4370.2940.4380.4070.3870.2120.4030.4370.2940.3440.0670.0730.0510.041
Marital_Status0.0000.0000.0000.0000.0000.0000.0900.0690.0480.0000.0000.0000.0000.0530.0000.0360.0001.0000.0000.0000.0000.0660.0000.0330.0000.0350.0240.0150.0000.0000.0000.0300.0520.1480.0120.000
MntFishProducts0.2700.0480.0760.0120.2630.1190.0290.0600.2860.0000.1320.0650.1790.480-0.0290.5780.3240.0001.0000.7040.5640.7260.6890.7000.6950.5220.656-0.1240.5810.6320.466-0.4600.0140.1280.1400.106
MntFruits0.2600.0000.0030.0700.2800.1310.0260.0310.2670.0000.1280.0730.1800.453-0.0180.5830.3130.0000.7041.0000.5690.7130.6740.6910.6830.5160.632-0.1120.5830.6270.473-0.4440.0250.1530.1200.094
MntGoldProds0.2000.0850.1260.0680.1780.1100.0760.0270.1610.0000.2250.0660.2070.253-0.0390.5060.2690.0000.5640.5691.0000.6390.6460.5400.6910.5750.6480.0910.5410.6440.577-0.2580.0170.1580.0620.178
MntMeatProducts0.3110.0330.0270.0960.3760.1590.1140.0850.3480.0000.1550.0550.2740.595-0.0110.8190.3220.0660.7260.7130.6391.0000.9430.6970.9400.8240.854-0.0340.7800.8650.683-0.4950.0270.2420.2260.084
MntRegularProds0.4070.1600.0640.2640.5200.2610.1670.1160.3230.0000.1730.0860.4170.533-0.0230.8620.4370.0000.6890.6740.6460.9431.0000.6620.9960.9370.887-0.0150.8150.9090.728-0.4790.0200.2900.2310.146
MntSweetProducts0.2560.0470.0000.0250.2660.117-0.0020.0410.2480.0000.1150.0690.1750.419-0.0300.5690.2940.0330.7000.6910.5400.6970.6621.0000.6690.5050.628-0.1080.5810.6250.462-0.4490.0240.1110.1010.063
MntTotal0.4180.1520.0560.2480.5240.2660.1600.1090.3240.0000.1830.0920.4200.532-0.0220.8530.4380.0000.6950.6830.6910.9400.9960.6691.0000.9280.894-0.0160.8060.9100.729-0.4760.0190.2940.2250.153
MntWines0.3560.3010.0940.3970.5180.3030.2360.1100.2170.0000.1550.1140.4730.348-0.0220.8320.4070.0350.5220.5160.5750.8240.9370.5050.9281.0000.8230.0540.8060.8690.742-0.3910.0170.2670.1120.153
NumCatalogPurchases0.3150.1120.0880.1900.3580.1900.1790.0820.2920.0000.1260.0650.3330.482-0.0090.7930.3870.0240.6560.6320.6480.8540.8870.6280.8940.8231.000-0.0440.7070.8720.621-0.5390.0290.2190.1190.095
NumDealsPurchases0.1670.0000.0000.0590.2440.0770.0880.0790.3680.0000.2160.0000.1360.564-0.027-0.1960.2120.015-0.124-0.1120.091-0.034-0.015-0.108-0.0160.054-0.0441.0000.0960.1070.2840.3950.0090.0980.3470.216
NumStorePurchases0.1950.0810.1810.2130.2290.1370.1700.1090.2010.0000.1140.1070.2450.316-0.0200.7350.4030.0000.5810.5830.5410.7800.8150.5810.8060.8060.7070.0961.0000.8870.673-0.4600.0040.1500.0850.095
NumTotalPurchase0.2700.1030.0660.2220.2880.1620.1810.1090.2340.0210.1580.1010.2990.353-0.0140.7800.4370.0000.6320.6270.6440.8650.9090.6250.9100.8690.8720.1070.8871.0000.833-0.4210.0120.1630.0760.153
NumWebPurchases0.1660.0000.0210.1590.1700.1100.1680.0920.1490.0000.2000.0820.2160.211-0.0230.5740.2940.0000.4660.4730.5770.6830.7280.4620.7290.7420.6210.2840.6730.8331.000-0.097-0.0020.1640.1600.186
NumWebVisitsMonth0.2040.0000.0770.0000.3060.093-0.1320.0860.3240.0000.3050.0540.1380.557-0.012-0.6440.3440.030-0.460-0.444-0.258-0.495-0.479-0.449-0.476-0.391-0.5390.395-0.460-0.421-0.0971.000-0.0190.1210.2170.246
Recency0.0000.0340.0440.0000.0000.0000.0170.0640.0310.0000.0260.0000.0000.021-0.0440.0100.0670.0520.0140.0250.0170.0270.0200.0240.0190.0170.0290.0090.0040.012-0.002-0.0191.0000.2090.0500.027
Response0.2940.1620.2510.1760.3220.4260.0650.0270.2030.0000.2010.0930.3660.2010.0340.2580.0730.1480.1280.1530.1580.2420.2900.1110.2940.2670.2190.0980.1500.1630.1640.1210.2091.0000.1590.171
Teenhome0.1470.0000.0380.0240.2040.0940.3360.3100.5640.0000.0000.1060.1040.6100.0000.3290.0510.0120.1400.1200.0620.2260.2310.1010.2250.1120.1190.3470.0850.0760.1600.2170.0500.1591.0000.018
YearsSinceEnrolled0.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.9610.0580.0000.0000.0000.0000.0410.0000.1060.0940.1780.0840.1460.0630.1530.1530.0950.2160.0950.1530.1860.2460.0270.1710.0181.000

Missing values

2024-09-30T22:19:51.367629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-30T22:19:52.163194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDEducationMarital_StatusIncomeKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseDaysSinceEnrolledYearsSinceEnrolledAgeAgeGroupMntTotalMntRegularProdsChildrenHasChildrenAcceptedCmpTotalHasAcceptedCmpNumTotalPurchase
05524Graduation058138.00058635885461728888381047000000166315746-6016171529000022
12174Graduation046344.01138111621621125000000011306046-60272121004
24141Graduation171613.00026426491271112142182104000000031204946-60776734000020
36182Graduation126646.0102611420103522046000000013903018-30534811006
45324PhD158293.010941734311846271555365000000016103331-45422407110014
57446Master162513.00116520429804214264106000000029304746-60716702110020
6965Graduation055635.001342356516450492747376000000059314331-45590563110017
76177PhD133454.01032761056312324048000000041712918-3016914611008
84855PhD130351.010191402433213029000000138814031-45464411005
95899PhD15648.0116828061113110020100000010806461+493621111
IDEducationMarital_StatusIncomeKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseDaysSinceEnrolledYearsSinceEnrolledAgeAgeGroupMntTotalMntRegularProdsChildrenHasChildrenAcceptedCmpTotalHasAcceptedCmpNumTotalPurchase
222910084Graduation124434.0209328200172212700000004204231-45503321005
22307004Graduation011012.0108224326712333129100000047013018-30846111116
22319817Master044802.0007185310143131020294128000000067714431-4510491029000025
22328080Graduation026816.0005051634310034000000068112818-30221900003
22348372Graduation134421.0108133762911027000000036304031-45302111003
223510870Graduation161223.00146709431824211824729345000000038114746-6013411094110016
22364001PhD164014.021564060300087825700010001906861+444436311115
22377270Graduation056981.0009190848217321224123136010000015503331-4512411217001118
22388235Master169245.001842830214803061265103000000015605846-60843782110021
22399405PhD152869.0114084361212133147000000162216046-6017215121008